Radiation Therapy (RT) is a highly utilized, efficacious and cost-effective therapeutic option for cancer patients. RT is received by up to two-thirds of all cancer patients in the US, has been estimated to be responsible for 40% of all cancer cures, yet represents only 5-10% of all cancer-related health expenditures1,2. In spite of its therapeutic importance, it is under-represented in the national portfolio of clinical trials (i.e. only 5.5% of NCI trials involve RT)2.
The sequencing of the human genome has paved the way for the era of precision medicine which promises that the right treatment will be delivered to the right patient at the right time. While the genomic era has affected the delivery of chemotherapy and targeted biological agents3 4 5, it has yet to impact RT, the single most utilized therapeutic agent in oncology6.
A central principle in precision medicine is that cancer therapy should be tailored to individual tumor biology7 8 9. In spite of this tenet, RT dose protocols are uniform or one-size-fits-all (e.g., a uniform daily dose rate of 2 Gray (“Gy”)) and have not yet been adapted to this vision. Thus, integrating individual biological differences into RT protocols is a central step towards realizing the promise of precision medicine, thereby improving RT-based clinical outcomes. Previously, a gene-expression based radiosensitivity index (RSI) was developed that has been validated in over 2,000 patients as a predictor of clinical outcome in RT-treated patients in multiple independent cohorts and disease sites10-19. These data support that clinical benefit from RT is non-uniform and only maximized in a sub-population of genomically-distinct patients (e.g. radiosensitive).
Personalized RT holds the promise that the diagnosis, prevention, and treatment of cancer can be based on individual assessment of risk.